Bayesian Inference in Dynamic Panel Stochastic Frontier Models

Jonuzaj, Mariol and Tsionas, Mike and Izzeldin, Marwan (2026) Bayesian Inference in Dynamic Panel Stochastic Frontier Models. Journal of the Royal Statistical Society: Series A Statistics in Society. ISSN 0964-1998 (In Press)

Text (BDPSFM_final_delivery_JRSSA_Revisions_January_2026)
BDPSFM_final_delivery_JRSSA_Revisions_January_2026.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (776kB)

Abstract

The paper develops a dynamic panel stochastic frontier model that incorporates firms’ intertemporal decision behaviour and short-run stagnant adjustments to the production process. Its dynamic specification recognises short-run output adjustment costs, where final output may be only partially adjusted to the optimum level. In nesting previous panel stochastic frontier models, our new approach delivers a flexible framework that accommodates heterogeneous technologies and latent time-varying inefficiency effects. In addition, our model handles endogeneity issues related to flexible inputs. Model inference is based on a Bayesian framework, where Markov Chain Monte Carlo (MCMC) techniques are utilized. Through extensive simulations, we demonstrate the robustness of the model in small and moderate samples. Last, we present our model in an empirical example, analysing publicly listed UK companies operating in the manufacturing and construction sector over the period 2004-2022. A general finding is that most firms exhibit stagnant production processes, with the half-life for adjusting supply to be as high as 6 quarters. The estimated average technical efficiency is 89%. Our findings underscore the importance of accounting for dynamic frictions and heterogeneity when evaluating firm performance and designing productivity-enhancing policies.